(D uctile) m 1.5 in 6 y E = 30 x 10 psi n .5 in ν = .3 3 d .5 in ρ = .284 lb/in c 5 in S y = 26 kpsi t .75 in q 170 psi q t
Page 22.198.15Public gen As IntegerPublic maxGen As IntegerPublic bestFV As DoublePublic bestFVIndex As IntegerPublic historyBestFV As DoublePublic historyBestFVIndex As IntegerPublic feasibilityArray() As DoublePublic x As DoublePublic y As IntegerPublic z As IntegerPublic t As IntegerPublic start As IntegerPublic finish As IntegerPublic feasibilityBestIndex As IntegerPublic feasibilityBestProjNum As IntegerPublic feasibilityBestWeight As IntegerPublic infeasibleIndex() As IntegerPublic infeasibleTemp() As IntegerPublic Sum As IntegerPublic infeasible As IntegerPublic q As IntegerPublic r As IntegerPublic Form As BooleanPublic sheetName As StringSub Main()'Student Assignment Problem'January 4, 2011
L 50 in 1010 H ot R olled Steel (D uctile) m 1.5 in 6 y E = 30 x 10 psi n .5 in ν = .3 3 d .5 in ρ = .284 lb/in c 5 in S y = 26 kpsi t .75 in q 170 psi q
). Let S be the set of all files in a target sampleand T ⊆ S × S the set of all plagiarized pairs. Given a match scoring function s : S × S → Q andan identical similarity function i : S → Q, the sensitivity preservation function on a similarityengine result set, p : P(S × S) → Q, is given by: 1 max({s(rα , rβ ) | (rα , rβ ) ∈ R ∧ {rα , rβ } = {tα ,tβ }})p(R) = |T | ∑ max(i(tα ), i(tβ )) (1) (tα ,tβ )∈T
c h n i q u e s i n a
the course is to develop modeling and analysis abilities ofstudents for the investigation of inventory, logistics, and supply chain problems faced by today'sfirms. The specific topics include a brief introduction into inventory management systems(focusing on definitions, inventory-related costs, motivations to keep inventory, and distributionvalue analysis); deterministic inventory modeling (concentrating on economic order quantity andits extensions); stochastic inventory modeling (describing single-period and multiple-periodnewsvendor models, (q,r) policy with backlogging and lost sales as well as service levels);logistics system design (transportation-related decisions and their impact on inventorydecisions); supply chain management and
) Engaging (2) Explaining (3) Q: Think about a time when you felt [excited, frustrated, impatient, etc.] with your team Valuing the this semester. Use the following prompts to reflect on that moment in time. 1) What Development happened? 2) How did it make you feel? 3) How did you interpret it, what role did you of Shared play, what role did others play, what caused you to see things differently? 4) If it was a Rules, positive experience what would you do in the future to make this happen again, if it was a Norms, negative experience what would you do next time to avoid this situation or deal with it Structure better? I: Student demonstrates I: Student demonstrates
: 57– 88.Erdil, N. O., Harichandran, R. S., Nocito-Gobel, J., Carnasciali, M., & Li, C. Q. (2016). Integrating e-learning modules into engineering courses to develop an entrepreneurial mindset in students. Proceedings of the 2016 ASEE Annual Conference & Exposition, New Orleans, LA.Erdil, N. O., Harichandran, R. S., Nocito-Gobel, J., Li, C. Q., & Carnasciali, M.I. (2017). Impact of integrated e-learning modules in developing an entrepreneurial mindset based on deployment at 25 institutions. Proceedings of the 2017 ASEE Annual Conference & Exposition, Columbus, OH.Fila, N. & Purzer, S. (2017). Exploring connections between engineering projects, student characteristics, and the
. Page 9.1286.6 Proceedings of the 2004 American Society for Engineering Education Annual Conference and Exposition Copyright 2004, American Society for Engineering Education Table 1: Sample Coding for a Single Research Communications Studio Session Event No. Start Time Event Type Speaker(s) Audience Code 1 0:01 OSF NT all G,S,Q 2 0:37 OSF MC NT S 3 0:38 Ps N/A N/A N/A
velocity V2 do not have the same valuesince the inlet and outlet pipe diameters are different. If the bulk flow rate Q is known, thevelocities V1 and V2 are easily calculated from the continuity relation: πD12 πD22 (5) Q = V1 = V2 4 4The efficiency η is found to be a function of the flow rate and is therefore not just a constant for agiven pump. Efficiency is an important characteristic of a pump, since, as indicated in Figure 2,the difference 𝑃𝑃𝑖𝑖𝑖𝑖 − 𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜 represents power that is lost.Fundamental variables appearing in pump characteristics curves
participant chose for their poem, and, therefore,some participants did not report their topic. Those responses are coded as undeclared. For theSpring 2023 semester, however, a separate question was included to ensure all participantsreported their topic in a clear manner (see Appendix B.2). Aside from undeclared, the codesfrom the analysis of deterministic inventory modeling in Poem 2 are preliminaries, ABCanalysis, EOQ modeling, and miscellaneous whereas the codes from the analysis of stochasticinventory modeling in Poem 3 are preliminaries, newsvendor, (q,r) policy, and miscellaneous.For participants who chose a topic that did not fall into one of the predetermined categories, theirresponse was tagged to miscellaneous. The miscellaneous reports
-for-a- flooding-system-in-student-learning.[19] Wu, D., P. Zhou, Z. Sun, and C.Q. Zhou. 2015. CFD analysis of lining erosion phenomenon at the outlet of top combustion hot blast stove. In: Proceedings of 2015 AISTech Conference, Cleveland, OH. Accessible from: http://digital.library.aist.org/pages/PR-368-113.htm.[20] Wang, Tenghao, Jichao Wang, Dong Fu, John Moreland, Chenn Q. Zhou, Yongfu Zhao, and Jerry C. Capo. 2015. Development of a virtual blast furnace training system. In: Proceedings of METEC-ESTAD 2015 Conference, Dusseldorf, Germany. Abstract available at: http://www.programmaster.org/PM/PM.nsf/ApprovedAbstracts/FCA132F29F76F65A85257CA700 7A22B5?OpenDocument.[21] Zhou, Chenn Q. 2013. Application of
. Nadeem, “STEM Jobs See Uneven Progress in Increasing Gender, Racial and Ethnic Diversity,” Pew Research Center Science & Society, Apr. 01, 2021. https://www.pewresearch.org/science/2021/04/01/stem-jobs-see-uneven-progress-in- increasing-gender-racial-and-ethnic-diversity/ (accessed Feb. 04, 2023).[2] “The STEM Gap: Women and Girls in Science, Technology, Engineering and Mathematics,” AAUW : Empowering Women Since 1881. https://www.aauw.org/resources/research/the-stem-gap/ (accessed Feb. 04, 2023).[3] J. Handelsman et al., “More women in science,” Science, vol. 309, no. 5738, Art. no. 5738, 2005.[4] S. E. Carrell, M. E. Page, and J. E. West, “Sex and science: How professor gender perpetuates the gender gap,” Q. J
/9781315258041.[18] S. A. Davis and R. P. Bostrom, “Training End Users: An Experimental Investigation of the Roles of the Computer Interface and Training Methods,” MIS Q., vol. 17, no. 1, p. 61, Mar. 1993, doi: 10.2307/249510.[19] S. A. Sukiman, H. Yusop, R. Mokhtar, and N. H. Jaafar, “Competition-Based Learning: Determining the Strongest Skill that Can Be Achieved Among Higher Education Learners,” Reg. Conf. Sci. Technol. Soc. Sci. (RCSTSS 2014), pp. 505–516, 2016, doi: 10.1007/978-981-10-1458-1_47.[20] G. Issa, S. M. Hussain, and H. Al-Bahadili, “Competition-Based Learning: A Model for © American Society for Engineering Education, 2023 2023 ASEE Annual
were three presentation formats based on the speakers’ style and timelimit. • Short form: 10 minute presentation followed by 15 minutes of Q&A per speaker • Long form: 25-30 minute presentation followed by 20-25 minutes of Q&A • Panel discussion: 5 minute presentation per panelist followed by open 20-25 minutes of Q&A for all panelistsUndergraduate students were given the option to receive course credit by either (1) asking twoquestions during class or (2) writing a one-paragraph summary for each speaker. Students wereable to miss up to two assignments and still receive a passing grade. Grading was pass/fail for allstudents.Both pre- and post-course surveys were administered online and students were asked to
abbreviated statements are shown in Figure 2. Categorical responses were quantified by assigning values of 1 through 5 for “Strongly Disagree” through “Strongly Agree,” respectively. No outliers were found in the data, using Q =1 (outliers are outside Q times the interquartile
I 5.0 5.0 0.0 100.0 100.0 1.2 1.0 3.9 K 23.0 21.0 2.0 91.3 91.0 1.8 1.0 3.3 L 4.0 4.0 0.0 100.0 100.0 1.0 1.0 4.3 M 21.0 14.0 7.0 66.7 66.0 2.1 1.0 3.7 N 269.0 142.0 127.0 52.8 52.8 1.4 1.0 1.8 O 97.0 87.0 10.0 89.7 87.6 2.6 1.0 4.1 P 14.0 13.0 1.0 92.9 90.0 5.1 1.0 13.2 Q 141.0 64.0 77.0 45.4 43.0 2.1
651, 92%response rate). Supplemental help sessions like Q&A sessions facilitated by the instructor andinstructor/peer leader office hours were rated neutral by 57% of the student respondents. Thiswas in line with the observation that students primarily sought help during the discussion sessionand these supplemental sessions were not well-attended. From Figure 2, 88% of the studentrespondents strongly agreed or agreed that their peer leader was a good guide/mentor and 93% ofthe students indicated that they could get help when they needed it. These results were an earlyindicator that the implementation of the in-person peer leader-led discussion sessions in smallergroups was a useful addition to the large-enrollment course
Teaching Module to Improve Student Understanding of Stakeholder Engagement Processes Within Engineering Systems Design. 57–67. https://doi.org/10.1007/978-3-319-32933-8_6Friedman, B., & Hendry, D. G. (2019). Value Sensitive Design: Shaping Technology with Moral Imagination. MIT Press. https://books.google.com/books?hl=en&lr=&id=8ZiWDwAAQBAJ&oi=fnd&pg=PR13&d q=value+sensitive+design+moral+imagination&ots=vchlHBMvLP&sig=FHupw7lAlTzwR _2hSj601EwARU8#v=onepage&q=value sensitive design moral imagination&f=falseFriedman, B., & Hendry, D. G. (2012). The Envisioning Cards: A Toolkit for Catalyzing Humanistic and Technical Imaginations. SIGCHI Conference on Human Factors in Computing
. 100-112, 2022.[12] O. Simpson, “Access, retention and course choice in online, open and distance learning”.European Journal of Open, Distance and E-learning, 7(1), 2004.[13] M. Scott, and D.A. Savage, “Lemons in the university: asymmetric information, academicshopping and subject selection”. Higher Education Research & Development, 41(4), pp. 1247-1261, 2022.[14] D. Bukhari, “Data science curriculum: Current scenario”. International Journal of DataMining & Knowledge Management Process, Vol. 10, 2020.[15] D. Li, E. Milonas, and Q. Zhang, “Content Analysis of Data Science Graduate Programs inthe US,” 2021 ASEE Virtual Annual Conference Content Access, 2021.[16] Z. Chen, X. Liu, and L. Shang, “Improved course recommendation algorithm
https://automeris.io/WebPlotDigitizer/ 5 https://www.youtube.com/watch?v=Mv5nqAPCKA4Figure 4: The number of hydrogen bonds per time (the left figure) and the number of amino acids partici-pating in helical (blue) and beta sheet (red) secondary structures per extension of the protein molecule (theright figure).parameters that are reported in Daggett’s paper and compare the drawn curves using the onlinegraphing calculator. How do these plotted graphs differ from the ones obtained from the nanoHubsimulations?DiscussionFollowup presentation and student Q&AWe had a followup presentation for the students who were involved with this PBL module wherethe first author did a presentation on the research efforts that lie at the intersection of
will inform future initiatives aimed at supportingthe academic journeys of female minority STEM students and ensuring their success.6.2 Initiative Two: ActivitiesActivity One: Panel Discussion and Q&A • Description: A panel discussion and Q&A session featuring minority female STEM professionals from various STEM disciplines will be organized. The objective of this panel is to allow these female STEM professionals to share the educational, professional and personal experiences, including the challenges faced and the successes achieved with female minority STEM students. • Goal: This event will provide female minority students with the opportunity to see themselves represented in the STEM fields and
datasets (pre-standard course, post-standard course, pre-honors course, post-honors course) and conductedstatistical tests to determine any differences within our datasets. significance testing, we analyzedfor any pre/post differences within and across course types via these statistical tests andconducted tests for normality via the Shapiro-Wilk Test and Q-Q plots. These normality testswere used to establish if parametric or nonparametric testing was appropriate. Nonparametrictests were used given normality testing indicated non-normal data for each variable(stakeholders, value categories, value created) in all four datasets. Thus, we applied theWilcoxon Signed Rank Test for pre/post significance testing within courses and the Mann-Whitney U Test
Director of Qeexo Week 8 - 15 Term Project (& ML Contest) Providing technical seminar and remote Q&A ▪ Topic selection - presentation sessions by engineering staff of Qeexo in ▪ Hands-on project development technical areas such as SW Installation and ▪ Final presentation Issue Resolutions.Term Project Description(s)Class term projects requested students to search for and choose project topics which could applyembedded ML to solve the relevant engineering problem(s). Term projects included three mainparts: Part I – ML Project Planning/Framing, Part II – ML Project Implementation, and Part III –Report and Presentation. Along with the course schedule, the major project
understanding becauseher understanding of the problem shifted and evolved during the idea generation and prototypingactivities of her project. Q: “Do you have any examples of different ways that you understood the problem as you were going through the project?” A: “So, in the beginning it was just broad concept because they already had a tag developed with the electronics. And so, in my mind, it was going to be like Okay, how do we take this tag that already exists and stick it on [animal] and then from like thinking about that conceptualizing prototyping. We kind of realized that the tag wasn’t actually doing to work at all and we’d have to redesign the electronics housing so then it turned into a
, questions, expert guidance, and coaching (Adams et al., 2017).More specifically, Northeastern’s IE Capstone program integrates multiple opportunities forteams to receive feedback. Regular assignments, weekly advisor meetings, frequent clientinteractions, coordinator check-in sessions, open class Q&A, peer-to-peer feedback, and writtenfaculty evaluations during presentations all serve as sources of feedback. The writing coordinatormeets with every team once a term to provide detailed feedback on the writing assignments, andassure the teams are poised to create high-quality documents. The writing program is describedin a recent Capstone Conference paper (McManus, 2022). Further, all teams are stronglyencouraged to seek out faculty members for